Sinem Kuz

Self-optimising Production Systems

One of the central success factors for production in high-wage countries is the solution of the conflict that can be described with the term “planning efficiency”. Planning efficiency describes the relationship between the expenditure of planning and the profit generated by these expenditures. From the viewpoint of a successful business management, the challenge is to dynamically find the optimum between detailed planning and the immediate arrangement of the value stream. Planning-oriented approaches try to model the production system with as many of its characteristics and parameters as possible in order to avoid uncertainties and to allow rational decisions based on these models. The success of a planning-oriented approach depends on the transparency of business and production processes and on the quality of the applied models. Even though planning-oriented approaches are supported by a multitude of systems in industrial practice, an effective realisation is very intricate, so these models with their inherent structures tend to be matched to a current stationary condition of an enterprise. Every change within this enterprise, whether inherently structural or driven by altered input parameters, thus requires continuous updating and adjustment. This process is very cost-intensive and time-consuming; a direct transfer onto other enterprises or even other processes within the same enterprise is often impossible. This is also a result of the fact that planning usually occurs a priori and not in real-time. Therefore it is hard for completely planning-oriented systems to react to spontaneous deviations because the knowledge about those naturally only comes a posteriori.

Automation of robotic assembly processes on the basis of an architecture of human cognition

A novel concept to cognitive automation of robotic assembly processes is introduced. An experimental assembly cell with two robots was designed to verify and validate the concept. The cell’s numerical control—termed a cognitive control unit (CCU)—is able to simulate human information processing at a rule-based level of cognitive control. To enable the CCU to work on a large range of assembly tasks expected of a human operator, the cognitive architecture SOAR is used. On the basis of a self-developed set of production rules within the knowledge base, the CCU can plan assembly processes autonomously and react to ad-hoc changes in assembly sequences effectively. Extensive simulation studies have shown that cognitive automation based on SOAR is especially suitable for random parts supply, which reduces planning effort in logistics. Conversely, a disproportional increase in processing time was observed for deterministic parts supply, especially for assemblies containing large numbers of identical parts.

Towards anthropomorphic movements for industrial robots

In order to increase productivity for processes that involve the interaction of human and robot, a promising approach is to increase the transparency of robot movements. Based on the hypothesis that anthropomorphic movements are more transparent to a human operator, this paper presents methodologies and techniques to generate humanlike movements for industrial robots.

Human-centered design of assistance systems for production planning and control: The role of the human in Industry 4.0

Industry 4.0 is an integrated approach that transfers available technologies and the concepts of digitization and networking from everyday life into the domain of industrial production. In this paper, the role of the human in Industry 4.0 is considered by means of a best practice approach. It shows what potential lies in the human-centered design of assistance systems in production planning and control. It involves the use of a tablet computer kitted with an Industry 4.0-compatible app on the shop-floor in a production environment. From empirical investigations, we have derived design recommendations on polarity and angular character height.

Design and Implementation of a Cognitive Simulation Model for Robotic Assembly Cells

Against the background of a changing global economy, new production technologies have to be developed to stay competitive in high-wage countries. Therefore, an integrated cognitive simulation model (CSM) has been developed to support the human operator and the assembly process. By making the behavior of the system more intuitive the cognitive compatibility between the operator and the production system is enhanced significantly. The presented CSM faces three different challenges: (1) visualizing the behavior of the system to give the human operator an understanding of the technical systems, (2) cognitive control of a real robotic assembly cell and (3) performing mass simulations in order to evaluate parameters, new assembly or planning strategies or the assembly of new products. Additionally, a graph-based planner supports the cognitive planning instance for realizing complex tasks.

Cognition-Enhanced, Self-optimizing Production Networks

This research area focuses on the management systems and principles of a production system. It aims at controlling the complex interplay of heterogeneous processes in a highly dynamic environment, with special focus on individualized products in high-wage countries. The project addresses the comprehensive application of self-optimizing principles on all levels of the value chain. This implies the integration of self-optimizing control loops on cell level, with those addressing the production planning and control as well as supply chain and quality management aspects. A specific focus is on the consideration of human decisions during the production process. To establish socio-technical control loops, it is necessary to understand how human decisions are made in diffuse working processes as well as how cognitive and affective abilities form the human factor within production processes.

Anthropomorphic design of robotic arm trajectories in assembly cells

BACKGROUND: Anthropomorphism is attribution of human form or behavior to non-human agents. Its application in a robot increases occupational safety and user acceptance and reduces the mental effort needed to anticipate robot behavior. OBJECTIVE: The research question focuses on how the anthropomorphic trajectory and velocity profile of a virtual gantry robot affects the predictability of its behavior in a placement task. METHODS: To investigate the research question, we developed a virtual environment consisting of a robotized assembly cell. The robot was given human movements, acquired through the use of an infrared based motion capture system. The experiment compared anthropomorphic and constant velocity profiles. The trajectories were based on human movements of the hand-arm system. The task of the participants was to predict the target position of the placing movement as accurately and quickly as possible. RESULTS: Results show that the anthropomorphic velocity profile leads to a significantly shorter prediction time (α = 0.05). Moreover, the error rate and the mental effort were significantly less for the anthropomorphic velocity profile. Based on these findings, a speed-accuracy trade-off can be excluded. CONCLUSIONS: Participants were able to estimate and predict the target position of the presented movement significantly faster and more accurately when the robot was controlled by the human-like velocity profile.

A Symbolic Approach to Self-optimisation in Production System Analysis and Control

With steadily increasing customer requirements on quality of both products and processes, companies are faced with increasing organisational and technical challenges. The market is characterised by individualised customer wishes which result in individual adaptations of the products. In order to manage this rapidly growing variety of products, the production system has to become much more flexible with respect to the product structure to be manufactured and the corresponding production and assembly processes. Especially in the field of assembly systems the increasing variety of products adds new complexities to the planning process and increases the costs, because (re-)planning efforts tend to grow exponentially to the number of variants.

Using anthropomorphism to improve the human-machine interaction in industrial environments (part II)

The idea of socio-technical systems emphasizes the mutual interrelationship between humans and technical system considering the human operator as an integral part of the system. However, to use the full potential of this idea the technical system has to be perceived and accepted as a team-partner. Anthropomorphism is a promising approach to improve the acceptance of non-human entities as team-partners. In the second part of this joint contribution we present a revised experimental setup of the studies presented in the first part. A virtual environment consisting of a robotized assembly cell was utilized to conduct a prediction experiment with nine subjects comparing anthropomorphic and robotic speed profiles on a gantry robot. As in the first part the task of the participants was to predict the target position during movement. The results show significant effects towards shorter prediction time and less errors when using anthropomorphic speed profiles.

Increasing Safety in Human-Robot Collaboration by Using Anthropomorphic Speed Profiles of Robot Movements

The demand for flexible production systems in which the flexibility at assembly processes is increased by human-robot collaboration rises. In such systems the safety of the worker, transparency of the robot’s actions and mental effort are of special importance. As acceptance of technical systems can be increased by anthropomorphism, an anthropomorphic speed profile of a simulated gantry robot is compared to conventional robotic trajectories. Results of a study with 20 male participants, in which the influence of these speed profiles for movements of the gantry robot on mental effort and prediction time was investigated, are presented in this paper. The results show a significant increase of accuracy for predicting target positions for the movements with an anthropomorphic speed profile. The speed profile does not have a significant effect on prediction times and mental effort. Hence, design recommendations for an ergonomic design of human-robot collaboration can be derived from these results.